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pyopenms

Analyze mass spectrometry data with Python and OpenMS.

Updated May 8, 2026
Search Tags:#data analysis#bioinformatics#metabolomics#mass spectrometry#proteomics
One-click install
npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill pyopenms-zeyuyang-0420
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pyopenms
Source: https://github.com/Zeyuyang-0420/bio-ai-research-skills/tree/main/categories/drug-discovery-molecular-modeling/pyopenms
Command: npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill pyopenms-zeyuyang-0420

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openms, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables users to process, analyze, and interpret mass spectrometry data using Python, facilitating proteomics and metabolomics research.

Core Features & Use Cases

  • Data Processing: Handle various mass spectrometry file formats and perform preprocessing tasks.
  • Feature Detection: Detect and link features across spectra and samples for quantitative analysis.
  • Identification: Integrate with search engines for peptide and protein identification.
  • Metabolomics Analysis: Perform untargeted metabolomics preprocessing and analysis.
  • Use Case: A researcher can use this Skill to analyze a mass spectrometry dataset, identify peptides and proteins, and perform metabolomics workflows like untargeted metabolomics analysis.

Quick Start

Load a mzML file using PyOpenMS and explore its contents.

import pyopenms as ms
exp = ms.MSExperiment()
ms.MzMLFile().load("sample.mzML", exp)

Frequently Asked Questions about pyopenms

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze mass spectrometry data in Python for proteomics and metabolomics?▼

You can analyze mass spectrometry data in Python by using the pyopenms library to handle file formats, detect features, and identify peptides and proteins. It relies on the OpenMS library for underlying computation.

Can I load and preprocess mzML files for untargeted metabolomics using Python?▼

Yes, you can load mzML files using Python by initializing MSExperiment and MzMLFile, then preprocess the data for untargeted metabolomics analysis. This includes feature detection and linking across samples.

Do I need to install the OpenMS library to run pyopenms workflows?▼

Yes, installing the OpenMS library is required because pyopenms relies on it for underlying computation. You need this environment setup to process mass spectrometry data and identify peptides.

What is the best way to detect and link features across mass spectrometry samples?▼

The best way to detect and link features across mass spectrometry samples is using pyopenms feature detection capabilities. This enables quantitative analysis by processing spectra and integrating search engine results.

Does pyopenms support peptide and protein identification with search engines?▼

Yes, pyopenms supports peptide and protein identification by integrating with search engines. This allows researchers to process mass spectrometry datasets and identify proteins within proteomics workflows.

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